Digital Transformation in Manufacturing...

Automation and Digital transformation have gone from slogan to a necessity in manufacturing and it all starts with gathering data and understanding where it is needed and what technologies can help maximize return on investment. Now manufacturing has realized it’s a “must have” if they want to stay in business. But this does not mean that digitization, digitalization, and digital transformation is new to them. The truth is that that they have been laying the groundwork for this over the course of several years. They may not feel that they have a full-blown smart factory in place just yet because of some outdated processes layered over that groundwork. But the basics are there, it’s all about roots. That’s why it makes so much sense for them to embrace all 3 D’s – digitization, digitalization, and digital transformation.

 

Here are few key elements of 3 D’s they always have in their back pocket –

  • Common Sense: Manufacturing & common sense have always gone hand in hand. Chances are things that don’t make sense simply aren’t going to work and thus aren’t worth their time. That’s the same approach they should use when it comes to 3 D’s. By letting common sense be their guiding light, they can avoid falling from empty promises and instead leverage ‘there D’s” in ways that line up with reality.

  • Waste Reduction: Manufacturing has a long history of trying to avoid waste. Just look at the entire concept of lean manufacturing. Its natural inclination toward doing things the simple way and can handy when they are planning their one of the “D’s”. After all, with so much new technology and so many solutions to shift through, it’s important to stay focused on decisions that will deliver value without creating waste in other areas.

  • Human Value: Say what you want about automation, artificial intelligence, and machine learning – technology is still no match for the human brain. People can do things that BOT will never be able to replicate. However, this instinctive protection of human value isn’t contrary to the goals of digital transformation, in fact it is complimentary. Digital transformation is not about replacing people with technology. Its all about meeting customer demand without the ability to increase the size of workforce. Using the existing workforce in smarter way and keep the companies alive.

So, what does this mean for manufacturing? Simple: they just need to go back to their roots and begin building their digital transformation strategy around the values they already have in place. With this approach, digital transformation feels like less of unknown to them.

 

Although they already have foundation for digital transformation in place as I said above, but it is also important for them to remember that this journey is all about improvement. This isn’t about taking their old habits and doing exact same thing on computer. It’s about expanding their horizons leveraging data analytics and new technologies of automation to make a deference in their production and supply chain environment.

 

Before I conclude, one question came to my mind – ‘what will happen when they complete’ their digital transformation journey? Because so far in this article we are talking only about constant progress.

 

“This journey doesn’t have a finish line” that’s because the whole idea is based on evolution; as the technology changes; the definition of digitization continues to expand, and digital transformation as a concept also grow to encompass new industry need and goals.

 

Digital transformation in manufacturing isn’t about building a cocoon, sitting, and waiting for change. Its about stepping up and taking active role in building the industry. While employing digital transformation framework it is also very important to avoid copying themselves or becoming “version 2.0”. Take those underlying values, erase the outdated processes layered on to top and build something new.


*****************
************
******

Comments

Post a Comment

Popular posts from this blog

Generative AI & Meta-Leaders

In the wake of advances in generative AI and large language models (LLMs), there is excitement about what the technology means for businesses. But we should not forget that this advancement is also posing a catch-22 situation to the traditional leadership. The transformative potential and rapid acceleration of generative AI are creating an imperative for them to act quickly. They should rethink their skillsets and develop new skillsets like – ability to make better sense of data insights, ability to ask better questions, ability to learn from smart mistakes etc. It is true that ChatGPT has taken the world by storm. In just 5 days since its launch in November last year, it gained one million users, a feat that no other popular online services have managed to achieve. But it is not the only generative AI tool, others include Midjourney and Dall-E for images generation, GitHub Copilot for coding, and even conditional generative adversarial networks (cGAN) such as Pix2Pix, developed by ...
Read more

Good automation! Select right process for RPA

Someone said very rightly – “Automation is good, so long as you know exactly where to put the machine.” In all my previous blogs I wrote about BOT failure, ROI Modelling, Cost saving, RPA Governance, CoE, POC, POV etc. But then I realized that before taking a deep dive into any of these subjects, first we need to have a right RPA process identification & prioritization methodology in place. Everybody across the industry agrees that Robotic process automation (RPA) can make a big difference for organizations tormented by repetitive routine processes – the kind that suck up the productivity of people whose time would be better put toward more important work. But many are confused by what seems to be a deceivingly simple question — which are the right processes for RPA? The poor choice of process for initial pilot is the leading root cause for failure to meet customers’ expectations. Robotic Process Automation (RPA) doesn’t suit every process and in majority of cases we don’t have...
Read more

RPA Exception Handling – Be in control

Companies in every major industry are turning to semiautonomous computers (better known as BOTs) to automate large- and small-scale business processes. This technology replaces human intervention in back-office operations, improving operational efficiency, reducing costs and increasing margins. However, organizations cannot employ this technology effectively unless the BOTs are engineered properly. And one of the most important component of this BOT engineering is Exception handling or in simple words right categorization of the errors and then developing solutions around the same to deal with those errors to meet the expectations of the RPA owner. Occasionally, in the course of its work, an RPA BOT definitely encounter a situation it was not programmed to handle – and, in those cases, it will stall. Those cases can be for foreseeable reasons – security or access constraints – or it can be for unforeseeable reasons – like missing or incorrect data. Increasingly, RPA developers are ab...
Read more